# -*- encoding: utf-8 -*- ''' @File : cogvideo_pipeline.py @Time : 2022/07/15 11:24:56 @Author : Wenyi Hong @Version : 1.0 @Contact : hwy22@mails.tsinghua.edu.cn ''' # here put the import lib import os import sys import torch import argparse import time from torchvision.utils import save_image import stat from icetk import icetk as tokenizer import logging, sys import torch.distributed as dist tokenizer.add_special_tokens(['', '', '']) from SwissArmyTransformer import get_args from SwissArmyTransformer.data_utils import BinaryDataset, make_loaders from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy from SwissArmyTransformer.generation.utils import timed_name, save_multiple_images, generate_continually from SwissArmyTransformer.resources import auto_create from models.cogvideo_cache_model import CogVideoCacheModel from coglm_strategy import CoglmStrategy def get_masks_and_position_ids_stage1(data, textlen, framelen): # Extract batch size and sequence length. tokens = data seq_length = len(data[0]) # Attention mask (lower triangular). attention_mask = torch.ones((1, textlen+framelen, textlen+framelen), device=data.device) attention_mask[:, :textlen, textlen:] = 0 attention_mask[:, textlen:, textlen:].tril_() attention_mask.unsqueeze_(1) # Unaligned version position_ids = torch.zeros(seq_length, dtype=torch.long, device=data.device) torch.arange(textlen, out=position_ids[:textlen], dtype=torch.long, device=data.device) torch.arange(512, 512+seq_length-textlen, out=position_ids[textlen:], dtype=torch.long, device=data.device) position_ids = position_ids.unsqueeze(0) return tokens, attention_mask, position_ids def get_masks_and_position_ids_stage2(data, textlen, framelen): # Extract batch size and sequence length. tokens = data seq_length = len(data[0]) # Attention mask (lower triangular). attention_mask = torch.ones((1, textlen+framelen, textlen+framelen), device=data.device) attention_mask[:, :textlen, textlen:] = 0 attention_mask[:, textlen:, textlen:].tril_() attention_mask.unsqueeze_(1) # Unaligned version position_ids = torch.zeros(seq_length, dtype=torch.long, device=data.device) torch.arange(textlen, out=position_ids[:textlen], dtype=torch.long, device=data.device) frame_num = (seq_length-textlen)//framelen assert frame_num == 5 torch.arange(512, 512+framelen, out=position_ids[textlen:textlen+framelen], dtype=torch.long, device=data.device) torch.arange(512+framelen*2, 512+framelen*3, out=position_ids[textlen+framelen:textlen+framelen*2], dtype=torch.long, device=data.device) torch.arange(512+framelen*(frame_num-1), 512+framelen*frame_num, out=position_ids[textlen+framelen*2:textlen+framelen*3], dtype=torch.long, device=data.device) torch.arange(512+framelen*1, 512+framelen*2, out=position_ids[textlen+framelen*3:textlen+framelen*4], dtype=torch.long, device=data.device) torch.arange(512+framelen*3, 512+framelen*4, out=position_ids[textlen+framelen*4:textlen+framelen*5], dtype=torch.long, device=data.device) position_ids = position_ids.unsqueeze(0) return tokens, attention_mask, position_ids def my_update_mems(hiddens, mems_buffers, mems_indexs, limited_spatial_channel_mem, text_len, frame_len): if hiddens is None: return None, mems_indexs mem_num = len(hiddens) ret_mem = [] with torch.no_grad(): for id in range(mem_num): if hiddens[id][0] is None: ret_mem.append(None) else: if id == 0 and limited_spatial_channel_mem and mems_indexs[id]+hiddens[0][0].shape[1] >= text_len+frame_len: if mems_indexs[id] == 0: for layer, hidden in enumerate(hiddens[id]): mems_buffers[id][layer, :, :text_len] = hidden.expand(mems_buffers[id].shape[1], -1, -1)[:, :text_len] new_mem_len_part2 = (mems_indexs[id]+hiddens[0][0].shape[1]-text_len)%frame_len if new_mem_len_part2 > 0: for layer, hidden in enumerate(hiddens[id]): mems_buffers[id][layer, :, text_len:text_len+new_mem_len_part2] = hidden.expand(mems_buffers[id].shape[1], -1, -1)[:, -new_mem_len_part2:] mems_indexs[id] = text_len+new_mem_len_part2 else: for layer, hidden in enumerate(hiddens[id]): mems_buffers[id][layer, :, mems_indexs[id]:mems_indexs[id]+hidden.shape[1]] = hidden.expand(mems_buffers[id].shape[1], -1, -1) mems_indexs[id] += hidden.shape[1] ret_mem.append(mems_buffers[id][:, :, :mems_indexs[id]]) return ret_mem, mems_indexs def my_save_multiple_images(imgs, path, subdir, debug=True): # imgs: list of tensor images if debug: imgs = torch.cat(imgs, dim=0) print("\nSave to: ", path, flush=True) save_image(imgs, path, normalize=True) else: print("\nSave to: ", path, flush=True) single_frame_path = os.path.join(path, subdir) os.makedirs(single_frame_path, exist_ok=True) for i in range(len(imgs)): save_image(imgs[i], os.path.join(single_frame_path, f'{str(i).rjust(4,"0")}.jpg'), normalize=True) os.chmod(os.path.join(single_frame_path,f'{str(i).rjust(4,"0")}.jpg'), stat.S_IRWXO+stat.S_IRWXG+stat.S_IRWXU) save_image(torch.cat(imgs, dim=0), os.path.join(single_frame_path,f'frame_concat.jpg'), normalize=True) os.chmod(os.path.join(single_frame_path,f'frame_concat.jpg'), stat.S_IRWXO+stat.S_IRWXG+stat.S_IRWXU) def calc_next_tokens_frame_begin_id(text_len, frame_len, total_len): # The fisrt token's position id of the frame that the next token belongs to; if total_len < text_len: return None return (total_len-text_len)//frame_len * frame_len + text_len def my_filling_sequence( model, args, seq, batch_size, get_masks_and_position_ids, text_len, frame_len, strategy=BaseStrategy(), strategy2=BaseStrategy(), mems=None, log_text_attention_weights=0, # default to 0: no artificial change mode_stage1=True, enforce_no_swin=False, guider_seq=None, guider_text_len=0, guidance_alpha=1, limited_spatial_channel_mem=False, # 空间通道的存储限制在本帧内 **kw_args ): ''' seq: [2, 3, 5, ..., -1(to be generated), -1, ...] mems: [num_layers, batch_size, len_mems(index), mem_hidden_size] cache, should be first mems.shape[1] parts of context_tokens. mems are the first-level citizens here, but we don't assume what is memorized. input mems are used when multi-phase generation. ''' if guider_seq is not None: logging.debug("Using Guidance In Inference") if limited_spatial_channel_mem: logging.debug("Limit spatial-channel's mem to current frame") assert len(seq.shape) == 2 # building the initial tokens, attention_mask, and position_ids actual_context_length = 0 while seq[-1][actual_context_length] >= 0: # the last seq has least given tokens actual_context_length += 1 # [0, context_length-1] are given assert actual_context_length > 0 current_frame_num = (actual_context_length-text_len) // frame_len assert current_frame_num >= 0 context_length = text_len + current_frame_num * frame_len tokens, attention_mask, position_ids = get_masks_and_position_ids(seq, text_len, frame_len) tokens = tokens[..., :context_length] input_tokens = tokens.clone() if guider_seq is not None: guider_index_delta = text_len - guider_text_len guider_tokens, guider_attention_mask, guider_position_ids = get_masks_and_position_ids(guider_seq, guider_text_len, frame_len) guider_tokens = guider_tokens[..., :context_length-guider_index_delta] guider_input_tokens = guider_tokens.clone() for fid in range(current_frame_num): input_tokens[:, text_len+400*fid] = tokenizer[''] if guider_seq is not None: guider_input_tokens[:, guider_text_len+400*fid] = tokenizer[''] attention_mask = attention_mask.type_as(next(model.parameters())) # if fp16 # initialize generation counter = context_length - 1 # Last fixed index is ``counter'' index = 0 # Next forward starting index, also the length of cache. mems_buffers_on_GPU = False mems_indexs = [0, 0] mems_len = [(400+74) if limited_spatial_channel_mem else 5*400+74, 5*400+74] mems_buffers = [torch.zeros(args.num_layers, batch_size, mem_len, args.hidden_size*2, dtype=next(model.parameters()).dtype) for mem_len in mems_len] if guider_seq is not None: guider_attention_mask = guider_attention_mask.type_as(next(model.parameters())) # if fp16 guider_mems_buffers = [torch.zeros(args.num_layers, batch_size, mem_len, args.hidden_size*2, dtype=next(model.parameters()).dtype) for mem_len in mems_len] guider_mems_indexs = [0, 0] guider_mems = None torch.cuda.empty_cache() # step-by-step generation while counter < len(seq[0]) - 1: # we have generated counter+1 tokens # Now, we want to generate seq[counter + 1], # token[:, index: counter+1] needs forwarding. if index == 0: group_size = 2 if (input_tokens.shape[0] == batch_size and not mode_stage1) else batch_size logits_all = None for batch_idx in range(0, input_tokens.shape[0], group_size): logits, *output_per_layers = model( input_tokens[batch_idx:batch_idx+group_size, index:], position_ids[..., index: counter+1], attention_mask, # TODO memlen mems=mems, text_len=text_len, frame_len=frame_len, counter=counter, log_text_attention_weights=log_text_attention_weights, enforce_no_swin=enforce_no_swin, **kw_args ) logits_all = torch.cat((logits_all, logits), dim=0) if logits_all is not None else logits mem_kv01 = [[o['mem_kv'][0] for o in output_per_layers], [o['mem_kv'][1] for o in output_per_layers]] next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(text_len, frame_len, mem_kv01[0][0].shape[1]) for id, mem_kv in enumerate(mem_kv01): for layer, mem_kv_perlayer in enumerate(mem_kv): if limited_spatial_channel_mem and id == 0: mems_buffers[id][layer, batch_idx:batch_idx+group_size, :text_len] = mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, :text_len] mems_buffers[id][layer, batch_idx:batch_idx+group_size, text_len:text_len+mem_kv_perlayer.shape[1]-next_tokens_frame_begin_id] =\ mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, next_tokens_frame_begin_id:] else: mems_buffers[id][layer, batch_idx:batch_idx+group_size, :mem_kv_perlayer.shape[1]] = mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1) mems_indexs[0], mems_indexs[1] = mem_kv01[0][0].shape[1], mem_kv01[1][0].shape[1] if limited_spatial_channel_mem: mems_indexs[0] -= (next_tokens_frame_begin_id - text_len) mems = [mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)] logits = logits_all # Guider if guider_seq is not None: guider_logits_all = None for batch_idx in range(0, guider_input_tokens.shape[0], group_size): guider_logits, *guider_output_per_layers = model( guider_input_tokens[batch_idx:batch_idx+group_size, max(index-guider_index_delta, 0):], guider_position_ids[..., max(index-guider_index_delta, 0): counter+1-guider_index_delta], guider_attention_mask, mems=guider_mems, text_len=guider_text_len, frame_len=frame_len, counter=counter-guider_index_delta, log_text_attention_weights=log_text_attention_weights, enforce_no_swin=enforce_no_swin, **kw_args ) guider_logits_all = torch.cat((guider_logits_all, guider_logits), dim=0) if guider_logits_all is not None else guider_logits guider_mem_kv01 = [[o['mem_kv'][0] for o in guider_output_per_layers], [o['mem_kv'][1] for o in guider_output_per_layers]] for id, guider_mem_kv in enumerate(guider_mem_kv01): for layer, guider_mem_kv_perlayer in enumerate(guider_mem_kv): if limited_spatial_channel_mem and id == 0: guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, :guider_text_len] = guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, :guider_text_len] guider_next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(guider_text_len, frame_len, guider_mem_kv_perlayer.shape[1]) guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, guider_text_len:guider_text_len+guider_mem_kv_perlayer.shape[1]-guider_next_tokens_frame_begin_id] =\ guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, guider_next_tokens_frame_begin_id:] else: guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, :guider_mem_kv_perlayer.shape[1]] = guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1) guider_mems_indexs[0], guider_mems_indexs[1] = guider_mem_kv01[0][0].shape[1], guider_mem_kv01[1][0].shape[1] if limited_spatial_channel_mem: guider_mems_indexs[0] -= (guider_next_tokens_frame_begin_id-guider_text_len) guider_mems = [guider_mems_buffers[id][:, :, :guider_mems_indexs[id]] for id in range(2)] guider_logits = guider_logits_all else: if not mems_buffers_on_GPU: if not mode_stage1: torch.cuda.empty_cache() for idx, mem in enumerate(mems): mems[idx] = mem.to(next(model.parameters()).device) if guider_seq is not None: for idx, mem in enumerate(guider_mems): guider_mems[idx] = mem.to(next(model.parameters()).device) else: torch.cuda.empty_cache() for idx, mem_buffer in enumerate(mems_buffers): mems_buffers[idx] = mem_buffer.to(next(model.parameters()).device) mems = [mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)] if guider_seq is not None: for idx, guider_mem_buffer in enumerate(guider_mems_buffers): guider_mems_buffers[idx] = guider_mem_buffer.to(next(model.parameters()).device) guider_mems = [guider_mems_buffers[id][:, :, :guider_mems_indexs[id]] for id in range(2)] mems_buffers_on_GPU = True logits, *output_per_layers = model( input_tokens[:, index:], position_ids[..., index: counter+1], attention_mask, # TODO memlen mems=mems, text_len=text_len, frame_len=frame_len, counter=counter, log_text_attention_weights=log_text_attention_weights, enforce_no_swin=enforce_no_swin, limited_spatial_channel_mem=limited_spatial_channel_mem, **kw_args ) mem_kv0, mem_kv1 = [o['mem_kv'][0] for o in output_per_layers], [o['mem_kv'][1] for o in output_per_layers] if guider_seq is not None: guider_logits, *guider_output_per_layers = model( guider_input_tokens[:, max(index-guider_index_delta, 0):], guider_position_ids[..., max(index-guider_index_delta, 0): counter+1-guider_index_delta], guider_attention_mask, mems=guider_mems, text_len=guider_text_len, frame_len=frame_len, counter=counter-guider_index_delta, log_text_attention_weights=0, enforce_no_swin=enforce_no_swin, limited_spatial_channel_mem=limited_spatial_channel_mem, **kw_args ) guider_mem_kv0, guider_mem_kv1 = [o['mem_kv'][0] for o in guider_output_per_layers], [o['mem_kv'][1] for o in guider_output_per_layers] if not mems_buffers_on_GPU: torch.cuda.empty_cache() for idx, mem_buffer in enumerate(mems_buffers): mems_buffers[idx] = mem_buffer.to(next(model.parameters()).device) if guider_seq is not None: for idx, guider_mem_buffer in enumerate(guider_mems_buffers): guider_mems_buffers[idx] = guider_mem_buffer.to(next(model.parameters()).device) mems_buffers_on_GPU = True mems, mems_indexs = my_update_mems([mem_kv0, mem_kv1], mems_buffers, mems_indexs, limited_spatial_channel_mem, text_len, frame_len) if guider_seq is not None: guider_mems, guider_mems_indexs = my_update_mems([guider_mem_kv0, guider_mem_kv1], guider_mems_buffers, guider_mems_indexs, limited_spatial_channel_mem, guider_text_len, frame_len) counter += 1 index = counter logits = logits[:, -1].expand(batch_size, -1) # [batch size, vocab size] tokens = tokens.expand(batch_size, -1) if guider_seq is not None: guider_logits = guider_logits[:, -1].expand(batch_size, -1) guider_tokens = guider_tokens.expand(batch_size, -1) if seq[-1][counter].item() < 0: # sampling guided_logits = guider_logits+(logits-guider_logits)*guidance_alpha if guider_seq is not None else logits if mode_stage1 and counter < text_len + 400: tokens, mems = strategy.forward(guided_logits, tokens, mems) else: tokens, mems = strategy2.forward(guided_logits, tokens, mems) if guider_seq is not None: guider_tokens = torch.cat((guider_tokens, tokens[:, -1:]), dim=1) if seq[0][counter].item() >= 0: for si in range(seq.shape[0]): if seq[si][counter].item() >= 0: tokens[si, -1] = seq[si, counter] if guider_seq is not None: guider_tokens[si, -1] = guider_seq[si, counter-guider_index_delta] else: tokens = torch.cat((tokens, seq[:, counter:counter+1].clone().expand(tokens.shape[0], 1).to(device=tokens.device, dtype=tokens.dtype)), dim=1) if guider_seq is not None: guider_tokens = torch.cat((guider_tokens, guider_seq[:, counter-guider_index_delta:counter+1-guider_index_delta] .clone().expand(guider_tokens.shape[0], 1).to(device=guider_tokens.device, dtype=guider_tokens.dtype)), dim=1) input_tokens = tokens.clone() if guider_seq is not None: guider_input_tokens = guider_tokens.clone() if (index-text_len-1)//400 < (input_tokens.shape[-1]-text_len-1)//400: boi_idx = ((index-text_len-1)//400 +1)*400+text_len while boi_idx < input_tokens.shape[-1]: input_tokens[:, boi_idx] = tokenizer[''] if guider_seq is not None: guider_input_tokens[:, boi_idx-guider_index_delta] = tokenizer[''] boi_idx += 400 if strategy.is_done: break return strategy.finalize(tokens, mems) class InferenceModel_Sequential(CogVideoCacheModel): def __init__(self, args, transformer=None, parallel_output=True): super().__init__(args, transformer=transformer, parallel_output=parallel_output, window_size=-1, cogvideo_stage=1) # TODO: check it def final_forward(self, logits, **kwargs): logits_parallel = logits logits_parallel = torch.nn.functional.linear(logits_parallel.float(), self.transformer.word_embeddings.weight[:20000].float()) return logits_parallel class InferenceModel_Interpolate(CogVideoCacheModel): def __init__(self, args, transformer=None, parallel_output=True): super().__init__(args, transformer=transformer, parallel_output=parallel_output, window_size=10, cogvideo_stage=2) # TODO: check it def final_forward(self, logits, **kwargs): logits_parallel = logits logits_parallel = torch.nn.functional.linear(logits_parallel.float(), self.transformer.word_embeddings.weight[:20000].float()) return logits_parallel def main(args): assert int(args.stage_1) + int(args.stage_2) + int(args.both_stages) == 1 rank_id = args.device % args.parallel_size generate_frame_num = args.generate_frame_num if args.stage_1 or args.both_stages: model_stage1, args = InferenceModel_Sequential.from_pretrained(args, 'cogvideo-stage1') model_stage1.eval() if args.both_stages: model_stage1 = model_stage1.cpu() if args.stage_2 or args.both_stages: model_stage2, args = InferenceModel_Interpolate.from_pretrained(args, 'cogvideo-stage2') model_stage2.eval() if args.both_stages: model_stage2 = model_stage2.cpu() invalid_slices = [slice(tokenizer.num_image_tokens, None)] strategy_cogview2 = CoglmStrategy(invalid_slices, temperature=1.0, top_k=16) strategy_cogvideo = CoglmStrategy(invalid_slices, temperature=args.temperature, top_k=args.top_k, temperature2=args.coglm_temperature2) if not args.stage_1: from sr_pipeline import DirectSuperResolution dsr_path = auto_create('cogview2-dsr', path=None) # path=os.getenv('SAT_HOME', '~/.sat_models') dsr = DirectSuperResolution(args, dsr_path, max_bz=12, onCUDA=False) def process_stage2(model, seq_text, duration, video_raw_text=None, video_guidance_text="视频", parent_given_tokens=None, conddir=None, outputdir=None, gpu_rank=0, gpu_parallel_size=1): stage2_starttime = time.time() use_guidance = args.use_guidance_stage2 if args.both_stages: move_start_time = time.time() logging.debug("moving stage-2 model to cuda") model = model.cuda() logging.debug("moving in stage-2 model takes time: {:.2f}".format(time.time()-move_start_time)) try: if parent_given_tokens is None: assert conddir is not None parent_given_tokens = torch.load(os.path.join(conddir, 'frame_tokens.pt'), map_location='cpu') sample_num_allgpu = parent_given_tokens.shape[0] sample_num = sample_num_allgpu // gpu_parallel_size assert sample_num * gpu_parallel_size == sample_num_allgpu parent_given_tokens = parent_given_tokens[gpu_rank*sample_num:(gpu_rank+1)*sample_num] except: logging.critical("No frame_tokens found in interpolation, skip") return False # CogVideo Stage2 Generation while duration >= 0.5: # TODO: You can change the boundary to change the frame rate parent_given_tokens_num = parent_given_tokens.shape[1] generate_batchsize_persample = (parent_given_tokens_num-1)//2 generate_batchsize_total = generate_batchsize_persample * sample_num total_frames = generate_frame_num frame_len = 400 enc_text = tokenizer.encode(seq_text) enc_duration = tokenizer.encode(str(float(duration))+"秒") seq = enc_duration + [tokenizer['']] + enc_text + [tokenizer['']] + [-1]*400*generate_frame_num text_len = len(seq) - frame_len*generate_frame_num - 1 logging.info("[Stage2: Generating Frames, Frame Rate {:d}]\nraw text: {:s}".format(int(4/duration), tokenizer.decode(enc_text))) # generation seq = torch.cuda.LongTensor(seq, device=args.device).unsqueeze(0).repeat(generate_batchsize_total, 1) for sample_i in range(sample_num): for i in range(generate_batchsize_persample): seq[sample_i*generate_batchsize_persample+i][text_len+1:text_len+1+400] = parent_given_tokens[sample_i][2*i] seq[sample_i*generate_batchsize_persample+i][text_len+1+400:text_len+1+800] = parent_given_tokens[sample_i][2*i+1] seq[sample_i*generate_batchsize_persample+i][text_len+1+800:text_len+1+1200] = parent_given_tokens[sample_i][2*i+2] if use_guidance: guider_seq = enc_duration + [tokenizer['']] + tokenizer.encode(video_guidance_text) + [tokenizer['']] + [-1]*400*generate_frame_num guider_text_len = len(guider_seq) - frame_len*generate_frame_num - 1 guider_seq = torch.cuda.LongTensor(guider_seq, device=args.device).unsqueeze(0).repeat(generate_batchsize_total, 1) for sample_i in range(sample_num): for i in range(generate_batchsize_persample): guider_seq[sample_i*generate_batchsize_persample+i][text_len+1:text_len+1+400] = parent_given_tokens[sample_i][2*i] guider_seq[sample_i*generate_batchsize_persample+i][text_len+1+400:text_len+1+800] = parent_given_tokens[sample_i][2*i+1] guider_seq[sample_i*generate_batchsize_persample+i][text_len+1+800:text_len+1+1200] = parent_given_tokens[sample_i][2*i+2] video_log_text_attention_weights = 0 else: guider_seq=None guider_text_len=0 video_log_text_attention_weights = 1.4 mbz = args.max_inference_batch_size assert generate_batchsize_total < mbz or generate_batchsize_total % mbz == 0 output_list = [] start_time = time.time() for tim in range(max(generate_batchsize_total // mbz, 1)): input_seq = seq[:min(generate_batchsize_total, mbz)].clone() if tim == 0 else seq[mbz*tim:mbz*(tim+1)].clone() guider_seq2 = (guider_seq[:min(generate_batchsize_total, mbz)].clone() if tim == 0 else guider_seq[mbz*tim:mbz*(tim+1)].clone()) if guider_seq is not None else None output_list.append( my_filling_sequence(model, args, input_seq, batch_size=min(generate_batchsize_total, mbz), get_masks_and_position_ids=get_masks_and_position_ids_stage2, text_len=text_len, frame_len=frame_len, strategy=strategy_cogview2, strategy2=strategy_cogvideo, log_text_attention_weights=video_log_text_attention_weights, mode_stage1=False, guider_seq=guider_seq2, guider_text_len=guider_text_len, guidance_alpha=args.guidance_alpha, limited_spatial_channel_mem=True, )[0] ) logging.info("Duration {:.2f}, Taken time {:.2f}\n".format(duration, time.time() - start_time)) output_tokens = torch.cat(output_list, dim=0) output_tokens = output_tokens[:, text_len+1:text_len+1+(total_frames)*400].reshape(sample_num, -1, 400*total_frames) output_tokens_merge = torch.cat((output_tokens[:, :, :1*400], output_tokens[:, :, 400*3:4*400], output_tokens[:, :, 400*1:2*400], output_tokens[:, :, 400*4:(total_frames)*400]), dim=2).reshape(sample_num, -1, 400) output_tokens_merge = torch.cat((output_tokens_merge, output_tokens[:, -1:, 400*2:3*400]), dim=1) duration /= 2 parent_given_tokens = output_tokens_merge if args.both_stages: move_start_time = time.time() logging.debug("moving stage 2 model to cpu") model = model.cpu() torch.cuda.empty_cache() logging.debug("moving out model2 takes time: {:.2f}".format(time.time()-move_start_time)) logging.info("CogVideo Stage2 completed. Taken time {:.2f}\n".format(time.time() - stage2_starttime)) # decoding # imgs = [torch.nn.functional.interpolate(tokenizer.decode(image_ids=seq.tolist()), size=(480, 480)) for seq in output_tokens_merge] # os.makedirs(output_dir_full_path, exist_ok=True) # my_save_multiple_images(imgs, output_dir_full_path,subdir="frames", debug=False) # torch.save(output_tokens_merge.cpu(), os.path.join(output_dir_full_path, 'frame_token.pt')) # os.system(f"gifmaker -i '{output_dir_full_path}'/frames/0*.jpg -o '{output_dir_full_path}/{str(float(duration))}_concat.gif' -d 0.2") # direct super-resolution by CogView2 logging.info("[Direct super-resolution]") dsr_starttime = time.time() enc_text = tokenizer.encode(seq_text) frame_num_per_sample = parent_given_tokens.shape[1] parent_given_tokens_2d = parent_given_tokens.reshape(-1, 400) text_seq = torch.cuda.LongTensor(enc_text, device=args.device).unsqueeze(0).repeat(parent_given_tokens_2d.shape[0], 1) sred_tokens = dsr(text_seq, parent_given_tokens_2d) decoded_sr_videos = [] for sample_i in range(sample_num): decoded_sr_imgs = [] for frame_i in range(frame_num_per_sample): decoded_sr_img = tokenizer.decode(image_ids=sred_tokens[frame_i+sample_i*frame_num_per_sample][-3600:]) decoded_sr_imgs.append(torch.nn.functional.interpolate(decoded_sr_img, size=(480, 480))) decoded_sr_videos.append(decoded_sr_imgs) for sample_i in range(sample_num): my_save_multiple_images(decoded_sr_videos[sample_i], outputdir,subdir=f"frames/{sample_i+sample_num*gpu_rank}", debug=False) os.system(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125") logging.info("Direct super-resolution completed. Taken time {:.2f}\n".format(time.time() - dsr_starttime)) return True def process_stage1(model, seq_text, duration, video_raw_text=None, video_guidance_text="视频", image_text_suffix="", outputdir=None, batch_size=1): process_start_time = time.time() use_guide = args.use_guidance_stage1 if args.both_stages: move_start_time = time.time() logging.debug("moving stage 1 model to cuda") model = model.cuda() logging.debug("moving in model1 takes time: {:.2f}".format(time.time()-move_start_time)) if video_raw_text is None: video_raw_text = seq_text mbz = args.stage1_max_inference_batch_size if args.stage1_max_inference_batch_size > 0 else args.max_inference_batch_size assert batch_size < mbz or batch_size % mbz == 0 frame_len = 400 # generate the first frame: enc_text = tokenizer.encode(seq_text+image_text_suffix) seq_1st = enc_text + [tokenizer['']] + [-1]*400 # IV!! # test local!!! # test randboi!!! logging.info("[Generating First Frame with CogView2]Raw text: {:s}".format(tokenizer.decode(enc_text))) text_len_1st = len(seq_1st) - frame_len*1 - 1 seq_1st = torch.cuda.LongTensor(seq_1st, device=args.device).unsqueeze(0) output_list_1st = [] for tim in range(max(batch_size // mbz, 1)): start_time = time.time() output_list_1st.append( my_filling_sequence(model, args,seq_1st.clone(), batch_size=min(batch_size, mbz), get_masks_and_position_ids=get_masks_and_position_ids_stage1, text_len=text_len_1st, frame_len=frame_len, strategy=strategy_cogview2, strategy2=strategy_cogvideo, log_text_attention_weights=1.4, enforce_no_swin=True, mode_stage1=True, )[0] ) logging.info("[First Frame]Taken time {:.2f}\n".format(time.time() - start_time)) output_tokens_1st = torch.cat(output_list_1st, dim=0) given_tokens = output_tokens_1st[:, text_len_1st+1:text_len_1st+401].unsqueeze(1) # given_tokens.shape: [bs, frame_num, 400] # generate subsequent frames: total_frames = generate_frame_num enc_duration = tokenizer.encode(str(float(duration))+"秒") if use_guide: video_raw_text = video_raw_text + " 视频" enc_text_video = tokenizer.encode(video_raw_text) seq = enc_duration + [tokenizer['']] + enc_text_video + [tokenizer['']] + [-1]*400*generate_frame_num guider_seq = enc_duration + [tokenizer['']] + tokenizer.encode(video_guidance_text) + [tokenizer['']] + [-1]*400*generate_frame_num logging.info("[Stage1: Generating Subsequent Frames, Frame Rate {:.1f}]\nraw text: {:s}".format(4/duration, tokenizer.decode(enc_text_video))) text_len = len(seq) - frame_len*generate_frame_num - 1 guider_text_len = len(guider_seq) - frame_len*generate_frame_num - 1 seq = torch.cuda.LongTensor(seq, device=args.device).unsqueeze(0).repeat(batch_size, 1) guider_seq = torch.cuda.LongTensor(guider_seq, device=args.device).unsqueeze(0).repeat(batch_size, 1) for given_frame_id in range(given_tokens.shape[1]): seq[:, text_len+1+given_frame_id*400: text_len+1+(given_frame_id+1)*400] = given_tokens[:, given_frame_id] guider_seq[:, guider_text_len+1+given_frame_id*400:guider_text_len+1+(given_frame_id+1)*400] = given_tokens[:, given_frame_id] output_list = [] if use_guide: video_log_text_attention_weights = 0 else: guider_seq = None video_log_text_attention_weights = 1.4 for tim in range(max(batch_size // mbz, 1)): start_time = time.time() input_seq = seq[:min(batch_size, mbz)].clone() if tim == 0 else seq[mbz*tim:mbz*(tim+1)].clone() guider_seq2 = (guider_seq[:min(batch_size, mbz)].clone() if tim == 0 else guider_seq[mbz*tim:mbz*(tim+1)].clone()) if guider_seq is not None else None output_list.append( my_filling_sequence(model, args,input_seq, batch_size=min(batch_size, mbz), get_masks_and_position_ids=get_masks_and_position_ids_stage1, text_len=text_len, frame_len=frame_len, strategy=strategy_cogview2, strategy2=strategy_cogvideo, log_text_attention_weights=video_log_text_attention_weights, guider_seq=guider_seq2, guider_text_len=guider_text_len, guidance_alpha=args.guidance_alpha, limited_spatial_channel_mem=True, mode_stage1=True, )[0] ) output_tokens = torch.cat(output_list, dim=0)[:, 1+text_len:] if args.both_stages: move_start_time = time.time() logging.debug("moving stage 1 model to cpu") model = model.cpu() torch.cuda.empty_cache() logging.debug("moving in model1 takes time: {:.2f}".format(time.time()-move_start_time)) # decoding imgs, sred_imgs, txts = [], [], [] for seq in output_tokens: decoded_imgs = [torch.nn.functional.interpolate(tokenizer.decode(image_ids=seq.tolist()[i*400: (i+1)*400]), size=(480, 480)) for i in range(total_frames)] imgs.append(decoded_imgs) # only the last image (target) assert len(imgs) == batch_size save_tokens = output_tokens[:, :+total_frames*400].reshape(-1, total_frames, 400).cpu() if outputdir is not None: for clip_i in range(len(imgs)): # os.makedirs(output_dir_full_paths[clip_i], exist_ok=True) my_save_multiple_images(imgs[clip_i], outputdir, subdir=f"frames/{clip_i}", debug=False) os.system(f"gifmaker -i '{outputdir}'/frames/'{clip_i}'/0*.jpg -o '{outputdir}/{clip_i}.gif' -d 0.25") torch.save(save_tokens, os.path.join(outputdir, 'frame_tokens.pt')) logging.info("CogVideo Stage1 completed. Taken time {:.2f}\n".format(time.time() - process_start_time)) return save_tokens # ====================================================================================================== if args.stage_1 or args.both_stages: if args.input_source != "interactive": with open(args.input_source, 'r') as fin: promptlist = fin.readlines() promptlist = [p.strip() for p in promptlist] else: promptlist = None now_qi = -1 while True: now_qi += 1 if promptlist is not None: # with input-source if args.multi_gpu: if now_qi % dist.get_world_size() != dist.get_rank(): continue rk = dist.get_rank() else: rk = 0 raw_text = promptlist[now_qi] raw_text = raw_text.strip() print(f'Working on Line No. {now_qi} on {rk}... [{raw_text}]') else: # interactive raw_text = input("\nPlease Input Query (stop to exit) >>> ") raw_text = raw_text.strip() if not raw_text: print('Query should not be empty!') continue if raw_text == "stop": return try: path = os.path.join(args.output_path, f"{now_qi}_{raw_text}") parent_given_tokens = process_stage1(model_stage1, raw_text, duration=4.0, video_raw_text=raw_text, video_guidance_text="视频", image_text_suffix=" 高清摄影", outputdir=path if args.stage_1 else None, batch_size=args.batch_size) if args.both_stages: process_stage2(model_stage2, raw_text, duration=2.0, video_raw_text=raw_text+" 视频", video_guidance_text="视频", parent_given_tokens=parent_given_tokens, outputdir=path, gpu_rank=0, gpu_parallel_size=1) # TODO: 修改 except (ValueError, FileNotFoundError) as e: print(e) continue elif args.stage_2: sample_dirs = os.listdir(args.output_path) for sample in sample_dirs: raw_text = sample.split('_')[-1] path = os.path.join(args.output_path, sample, 'Interp') parent_given_tokens = torch.load(os.path.join(args.output_path, sample, "frame_tokens.pt")) process_stage2(raw_text, duration=2.0, video_raw_text=raw_text+" 视频", video_guidance_text="视频", parent_given_tokens=parent_given_tokens, outputdir=path, gpu_rank=0, gpu_parallel_size=1) # TODO: 修改 else: assert False if __name__ == "__main__": logging.basicConfig(stream=sys.stderr, level=logging.DEBUG) py_parser = argparse.ArgumentParser(add_help=False) py_parser.add_argument('--generate-frame-num', type=int, default=5) py_parser.add_argument('--coglm-temperature2', type=float, default=0.89) # py_parser.add_argument("--interp-duration", type=float, default=-1) # -1是顺序生成，0是超分，0.5/1/2是插帧 # py_parser.add_argument("--total-duration", type=float, default=4.0) # 整个的时间 py_parser.add_argument('--use-guidance-stage1', action='store_true') py_parser.add_argument('--use-guidance-stage2', action='store_false') py_parser.add_argument('--guidance-alpha', type=float, default=3.0) py_parser.add_argument('--stage-1', action='store_true') # stage 1: sequential generation py_parser.add_argument('--stage-2', action='store_false') # stage 2: interp + dsr py_parser.add_argument('--both-stages', action='store_false') # stage 1&2: sequential generation; interp + dsr py_parser.add_argument('--parallel-size', type=int, default=1) py_parser.add_argument('--stage1-max-inference-batch-size', type=int, default=1) # -1: use max-inference-batch-size py_parser.add_argument('--multi-gpu', action='store_false') CogVideoCacheModel.add_model_specific_args(py_parser) known, args_list = py_parser.parse_known_args() args = get_args(args_list) args = argparse.Namespace(**vars(args), **vars(known)) args.layout = [int(x) for x in args.layout.split(',')] args.do_train = False torch.cuda.set_device(args.device) with torch.no_grad(): main(args)